Future holds for Data-driven Factories! Keep your SME ready

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According to Wikipedia, “The adjective data-driven means that progress in an activity is compelled by data, rather than by intuition or personal experience. It is often labeled as business jargon for what scientists call evidence-based decision making.”

It has become important for SMEs to run their manufacturing industries using data-driven decisions. These decisions can help them to make fast, smart decisions, thus stay on top of the market. But what should be an ideal approach to collect data on their processes or machines?

Transforming the Technology

With so much advancement in the latest technology, it has become a basic necessity for the manufacturing industry to have data-driven factory (for the better and secure future), which comprises of internal and external activities connected through the same information platform. All the entities (like customers, designers, operators) share information on everything, such as from installation to performance feedback.

To capture emerging opportunities and keep up with the advanced technology, SMEs need to act on the following dimensions:

Collect data

Manual approach of collecting data means putting extra efforts to collect information by direct observation, interviews, surveys, experiments and testing, or other methods. This approach can also mean that there was a month of data not taken into account for predictions and modeling.

SMEs should realize that there are ways to collect data through latest technologies, like Internet of Things (IoT). IoT will help in tracking real-time behavior, sensor-driven analytics, process optimization, etc. in a fraction of the time.

Analyze data

Equal and importance to data collection is a use of analytical software tools that can help slice and dice the data and assist in arriving at decisions that aid manufacturing or support of product and services for the SMEs.

Use cases of Data-Driven Factories

Collect real time data of product unit sales to understand and analyze the demand at the customer end instead of only at the middle layers (for e.g., distribution centers, retail outlets, etc.) and align supply chain based on how many units are being sold from where.

Collection of status and health information of tools that help in manufacturing the units so as to avoid downtime, especially in industries, where a downtime can lead to significant revenue loss.

Use of data collection techniques on the products themselves (for e.g., a product that experiences wear and tear and sensors that collect this information about the product). This information may be used to alert the customer, when a product unit needs servicing. It may also be used, when the unit is taken to a service center and help in identifying where the wear and tear is actually happening.

Conclusion

The technical transformation has become imperative, which requires SMEs to prepare themselves for upcoming data-driven factories where internal and external activities are connected through the same information platform.